Iranian Journal of Remote Sensing & GIS

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The Effect of Atmospheric Moisture on the Estimation of the Temperature Inversion Characteristics from MODIS Images

H Kachar; M.R Mobasheri; A.A Abkar; M Rahimzadegan

Volume 7, Issue 2 , November 2015, , Pages 35-53

Abstract

Increase of temperature with height in the troposphere is called temperature inversion. Parameters such as strength and depth are characteristics of temperature inversion. Inversion strength is defined as the temperature difference between the surface and the top of the inversion and the depth of inversion ... Read More Increase of temperature with height in the troposphere is called temperature inversion. Parameters such as strength and depth are characteristics of temperature inversion. Inversion strength is defined as the temperature difference between the surface and the top of the inversion and the depth of inversion is defined as the height of the inversion from the surface. The common approach in determination of these parameters is field measurements by Radiosonde. On the other hand the Radiosonde data are too sparse, so using satellite images is essential for modeling the temperature inversion. Necessary condition for the temperature inversion modeling using satellite images, examine the relationship between the brightness temperature difference with the temperature inversion strength and depth of the resulting data is Radiosonde. Temperature inversion phenomenon is common in Tehran. So Mehrabad airport weather station was selected as the 1st study area. Then correlation coefficients between Brightness temperature differences of different band pairs and the inversion depth and strength collected by Radiosonde were calculated. The results showed weak linear correlation. This could be due to the change of the atmospheric water vapor content and the relatively weak temperature inversion strength and depth occurred in Tehran. Proving this hypothesis is an innovation in the present work, in continuation of this research, the factors increasing the linear correlation coefficient was investigated. Due to the presence of deeper and stronger temperature inversion in Kermanshah, this region was chosen as the second studied region. The calculated correlation coefficients increased for Kermanshah all due to increase in the strength and depth of the temperature inversion in this region. Knowing that the amount of water vapor in the atmosphere in winter is less than warm seasons, Tehran and Kermanshah data were divided into two all seasons and cold seasons.Increase of correlation coefficients for both Tehran and Kermanshah in the cold season verifies the effect of atmospheric water content. For instance, the correlation coefficient between BT7.2-BT11 with strength and depth of inversion for Kermanshah for all season are 0.51 and 0.70 respectively. This for cold season was boosted to 0.78 and 0.85.

Alteration Mapping in the Motrabad Epithermal System, South of Razavi Khorasan Province: using ASTER remote sensing data

Volume 6, Issue 1 , April 2014

Abstract

The Motrabad epithermal system 30 km southwest of Bajestan is located in an assemblage of intermediate to silicic volcanic rocks. The mineralization occurs as irregular veins, veinlets and hydrothermal breccias. Hydrothermal alteration is developed around the veins and consists of silicic ( Read More

Due to the rapid population growth and urban development which results in limited arable land, land evaluation for selecting optimum utilization of land and increase in production per unit area as well as purposeful decisions in allocation of agricultural

Volume 6, Issue 1 , April 2014

Abstract

Due to the rapid population growth and urban development which results in limited arable land, land evaluation for selecting optimum utilization of land and increase in production per unit area as well as purposeful decisions in allocation of agricultural land to the best land use type is very important. ... Read More

Land Use Classification Based on Conceptual Features

Volume 6, Issue 1 , April 2014

Abstract

Nowadays, urban land use and land cover information at the micro and macro levels of planning have a particular importance. So many researches have not been done in land use information extraction. Remote sensing as an inexpensive and fast method, and particular with appearance of object-based analysis, ... Read More

Estimating Chlorophyll a and Total Phosphorous in Anzali Wetland Using Three Band Model

Volume 6, Issue 1 , April 2014

Abstract

Field spectrometry as a field of remote sensing, dealing with determination of spectral characteristics tries to provide the spectral libraries for different objects. The first objective of this study was to prepare and investigate the significant differences between the spectral signature of water samples ... Read More

Unsupervised Change Detection of Multi-temporal Satellite Images using PSO-based Image Thresholding Technique

Volume 6, Issue 1 , April 2014

Abstract

Timely and accurate detection of changes in land use/ cover is important for land planning and management. Remote sensing images have been primary sources for change detection in recent decades. Due to its simplicity, thresholding of difference image is a popular method for change detection. The traditional ... Read More Timely and accurate detection of changes in land use/ cover is important for land planning and management. Remote sensing images have been primary sources for change detection in recent decades. Due to its simplicity, thresholding of difference image is a popular method for change detection. The traditional thresholding methods such as Otsu are based on exhaustive search, so that they are time consuming. Since these methods are mainly developed for one-dimensional problems, the computation time grows exponentially with the number of thresholds when these methods are extended to be used for multi-dimensional problems. If thresholding is supposed to be as an optimization problem, optimization methods can potentially decrease the computation time. In this paper, a fast, simple and effective multi-dimensional image thresholding technique based on Particle Swarm Optimization (PSO) method is presented. This technique calculates the optimal threshold values by maximizing the Otsu objective function and minimizing the inter-class variance objective function. The proposed method has been implemented on two multispectral and multi-temporal datasets. The first dataset includes a couple of images acquired by the TM sensor taken form south islands of Aurmia Lake (Iran) in Jun 1984 and July 2010, respectively. The second dataset is obtained from a couple of images acquired by the same sensor on the Khodafarin dam (Iran) in July 2000 and July 2009, respectively. In order to evaluate the proposed method, the computational time and change detection accuracy were computed. In addition, statistical test was carried out in order to evaluate the robustness of the developed method. The experimental results show that the proposed PSO-based multi-dimensional thresholding method could provide optimum thresholds values by decreasing 98% and 15% of the time complexity compared with the most widely used Otsu and inter-class variance-based thresholding methods.

Snow covered surfaces show enormous variations in time and consequently need to be monitored using images with relatively high temporal resolution. For this MODIS sensor on board of Terra/Aqua is suitable. Different parameters affect the accuracy of Snow

Volume 6, Issue 1 , April 2014

Abstract

Snow covered surfaces show enormous variations in time and consequently need to be monitored using images with relatively high temporal resolution. For this MODIS sensor on board of Terra/Aqua is suitable. Different parameters affect the accuracy of Snow Covered Surface estimation (SCS) where the surface ... Read More

A New Model for Geometrical Correction of Satellite Imagery, Using 2D Polynomials and Satellite Navigation Data

Volume 4, Issue 3 , September 2012

Abstract

This paper proposes a new approach for geometrical modeling of satellite imagery which uses 2D-polynomials for 3D point determination from satellite stereo images. In this model, 2D polynomials are considered as additional parameters in colinearity equation, instead of considering as models for relating ... Read More

A Novel Approach for Optimal Feature Selection and Sub-Pixel Mapping in Polarimetric Radar Images

Volume 4, Issue 1 , March 2012

Abstract

Taking the advantages of polarimetric radar data has a decisive role in target detection purposes. In this way, comprehensive geometric and descriptive information could be derived through processing this kind of data. However, the selection of optimal features could be considered as a major challenge ... Read More Taking the advantages of polarimetric radar data has a decisive role in target detection purposes. In this way, comprehensive geometric and descriptive information could be derived through processing this kind of data. However, the selection of optimal features could be considered as a major challenge in order to classification of the polarimetric radar imagery. In this paper, a novel approach is proposed for optimal feature selection based on mapping the extracted features to the prototype space. As a key result of the paper, fitness index is introduced to facilitate the optimal feature selection in polarimetric radar images. On the other hand, the mixture of backscattering mechanisms in a pixel level is another limitation to obtain precise spatial information. Thus, utilizing soft classifiers is indispensible to acquire the sub-pixel information. Positivity and sum to unity of the fractions within each pixel are major challenges in results of the soft classifiers. In this paper, integration of the soft classifiers and unsupervised algorithms of end-member extraction is proposed to solve this problem. Likewise, soft classifiers just provide fractional maps and the spatial arrangement of sub-pixels remains unknown. In this regard, Super Resolution Mapping (SRM) techniques are developed to enhance the spatial resolution of the results of soft classifiers. This research attempts to provide a sub-pixel classification of polarimetric radar images using the pixel swapping technique. Towards this end, a non-random procedure is suggested for initial arrangement of the sub-pixels. According to the results, the proposed method for optimal feature selection is demonstrated more accurate results than genetic algorithm. Next, three algorithms including Linear Spectral Unmixing (LSU), Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) are performed to soft classifying of the polarimetric radar image into three classes (residential, vegetation and bare earth). SVM present accurate results in comparison to others; its resulted fractional maps are used in SRM procedure. Finally, pixel swapping technique is performed based on the results of SVM classification and the land cover map of the study area is produced in a finer spatial resolution.